Helping AI Agents Remember: Managing Context and State in Long-Running Projects
Published:
You’re three hours into a complex refactoring. Claude Code has successfully completed phases 1 and 2: extracting interfaces and updating your data models. You ask it to start phase 3, implementing the new repository pattern. It responds with code that completely ignores the architectural decision you made in phase 1 about maintaining backwards compatibility.
The frustration is immediate and familiar. You’ve just spent 15 minutes explaining the context, only to have the agent lose the thread at the critical moment. This isn’t a one-off problem. Every developer using AI agents for complex work has hit this wall: the agent that was brilliant five minutes ago suddenly can’t remember the core constraint that shaped the entire project.
This context collapse isn’t just annoying; it fundamentally limits what we can accomplish with AI assistance. Simple, single-session tasks work beautifully. But the moment projects span multiple phases, require decisions that build on previous decisions, or stretch across several work sessions, most developers retreat to doing the complex orchestration themselves. The promise of AI-assisted development crashes into the reality of agents that can’t maintain continuity.
I’ve written about related challenges before: managing context with templates, breaking work into bounded tasks, and creating decision documentation for teams and AI. Those posts tackled important pieces: how to structure single interactions, how to decompose work effectively, how to document decisions for team-level knowledge.
But they all assumed something simpler than what happens in real projects: continuity across multiple sessions, maintaining state through multi-phase implementations, and helping agents recover when they lose their way mid-project. That’s the harder problem this post addresses.
The good news? The solution isn’t magical future AI capabilities. It’s systematic approaches you can implement today: external memory systems, structured handoff protocols, checkpoint-driven development, and recovery strategies. By the end of this post, you’ll have specific techniques to maintain continuity across complex projects, turning frustrating context collapse into reliable AI partnership.
Why AI Agents Lose Context
Understanding why agents struggle with continuity helps us design better solutions. The problem isn’t that AI is “bad at remembering”. It’s that the way agents process information doesn’t align naturally with how multi-phase projects unfold.
Think of an AI agent’s context window like a novelist trying to hold an entire plot in their head whilst writing chapter 15. They have excellent reasoning about what’s immediately in front of them, but earlier plot points, character motivations, and thematic decisions gradually fade from active consideration. The information might technically be accessible, but it’s no longer driving their thinking.
This working memory limitation manifests in several ways. When you start a new conversation session, even if all your project files are available, the agent faces a cold start problem. It can read ARCHITECTURE.md and CONTRIBUTING.md, but without actively maintained context about where you are in the project, what decisions shaped earlier phases, and what constraints matter right now, it’s essentially starting fresh. Having access to information isn’t the same as understanding how that information connects to the current task.
The planning fallacy makes this worse. Multi-phase plans sound brilliant at the start: “First we’ll extract interfaces, then update the models, then refactor the repositories, then migrate the data.” Each phase sounds straightforward. But phase 3 depends on remembering the architectural decision from phase 1, and phase 4 needs to know about the compromise you made in phase 2. Agents can’t naturally “look back” at their own previous reasoning the way humans do. They process what’s in the current context window, not what was discussed an hour ago unless you explicitly bring it forward.
This creates the frustrating pattern every developer recognises: the agent that was perfect for phase 1 suddenly makes nonsensical suggestions in phase 3 because it’s lost the thread. The capability didn’t change; the context continuity broke.
The key insight? This isn’t an agent limitation to complain about; it’s a system design challenge to solve. Just as we don’t expect databases to hold all data in RAM, we shouldn’t expect agents to hold all project context in their working memory. We need external systems that agents can reference, structured handoff protocols that reload context efficiently, and checkpoint patterns that create resumable project states.
Strategy 1: External Memory Systems
The first principle of maintaining AI context is simple: don’t rely on the agent to remember everything. Create external systems that persist between sessions and that agents can actively reference. I’ve written before about decision documentation for teams and context templates for single interactions. This strategy adapts those concepts for ongoing project continuity.
The Project Notebook Pattern
Create a PROJECT_STATE.md file in your project root. This isn’t another static architecture document; it’s a living record of where you are right now in the project. Mine typically includes:
- Current Goal: What we’re working towards this week (not the entire project vision)
- Active Phase: Which checkpoint we’re in and what needs to happen before it’s complete
- Recent Decisions: The last 3-5 architectural or implementation choices with brief rationale
- Next Steps: Specific, actionable items for the next session
- Open Questions: Things we need to resolve before proceeding
This works alongside your existing documentation. The Compass Pattern taught us to reference ARCHITECTURE.md and CONTRIBUTING.md rather than duplicate them. PROJECT_STATE.md complements those static docs by capturing the dynamic state: where you are in the journey, not just what the destination looks like.
Update this file as you work, not just at the end of sessions. When you make a significant architectural decision, add it. When you complete a phase, mark it. When you discover a blocker, document it. This continuous maintenance ensures the file always reflects current reality.
Decision Logs for Context
Beyond the high-level project state, maintain a simple decision log. For team-level implementation, see my post on documentation as decision history. For long-running AI collaboration, the key difference is capturing why in ways agents can quickly parse:
1## 2025-01-15: Repository Pattern Choice
2
3**Decision**: Use generic repository with specification pattern
4**Rationale**: Maintains backwards compatibility with existing queries
5**Alternatives Considered**: Entity-specific repositories (more boilerplate), direct DbContext access (couples business logic)
6**Impact**: All new data access must use specifications
Date-stamped entries let agents understand decision chronology. When phase 3 contradicts phase 1, the agent can reference the log to understand the evolution.
Progress Tracking Beyond TODO Lists
Standard TODO lists fail for AI collaboration because they lack context. “Implement authentication” tells you nothing about why, what decisions shaped the approach, or what constraints matter. Context-rich progress tracking solves this:
1# Project Progress
2
3## Phase 1: Interface Extraction ✅
4
5**Completed**: 2025-01-10
6**Goal**: Extract IAuthProvider interface for provider-agnostic auth
7**Outcome**: Generic interface supporting multiple future providers
8**Key Decision**: Async-first design to support OAuth2 token operations
9**Next Phase Dependency**: OAuth2Provider implementation must follow this interface
10
11## Phase 2: OAuth2 Implementation 🔄
12
13**Started**: 2025-01-14
14**Goal**: Working OAuth2 authentication
15**Progress**:
16
17- ✅ Token exchange flow
18- ✅ Refresh token logic
19- 🔄 Error handling (in progress)
20- ⏳ Integration testing (next)
21 **Blocker Resolved**: Initially planned persistent caching, decided in-memory sufficient for MVP
This format gives agents the context they need: what’s complete, why decisions were made, what comes next. When you resume work in Phase 3, the agent can read this progress log and immediately understand the project trajectory.
Practical Implementation
Store these files in your project root where agents naturally find them. I use this structure:
/project-root
PROJECT_STATE.md # Current state and active work
DECISIONS.md # Chronological decision log
PROGRESS.md # Phase tracking with context
/session-notes # Individual session handoffs
2025-01-10.md
2025-01-14.md
Update PROJECT_STATE.md whenever you make decisions agents will need to remember. The session notes directory creates a breadcrumb trail of your journey through the project. When an agent asks “What did we decide about caching?”, you can point to the specific session note or decision log entry.
Get agents to use these files through explicit prompting: “Before we start, please read PROJECT_STATE.md and PROGRESS.md to understand where we are in the project. We’re starting Phase 3 today.”
The difference between a project with and without external memory is stark. Without it, every session starts with 10 minutes of “Here’s what we’re doing and why…” With it, agents load context in seconds and immediately contribute meaningfully. You’re not working harder; you’re working systematically.
Integration with Existing Workflows
These files complement, not replace, your existing documentation. As I explored in The Compass Pattern, ARCHITECTURE.md and CONTRIBUTING.md remain your canonical sources for system design and development workflows. PROJECT_STATE.md and related files capture the dynamic journey through implementation, whilst the Compass Pattern files document the static destination.
For teams using Git for decision tracking (see Documentation as Decision History), these files integrate naturally. Commit messages reference decisions from DECISIONS.md. Tags mark checkpoint completions. Branch names align with checkpoint phases. The systems reinforce each other rather than competing.
Strategy 2: Structured Handoff Protocols
Session boundaries kill context. You finish productive work on Friday afternoon, return Monday morning, and face the cold start problem: the agent has no memory of where you left off. Structured handoff protocols solve this by creating systematic ways to capture and reload context efficiently.
The Session End Routine
Before closing a work session, spend five minutes creating a handoff document. This isn’t the same as PROJECT_STATE.md (which tracks overall project status); it’s a session-specific summary:
1## Session 2025-01-15
2
3**Completed**: Extracted auth interfaces, created IAuthProvider abstraction
4**Decisions**: Generic interface over provider-specific; supports async throughout
5**Next**: Implement OAuth2Provider class following new interface
6**Blockers**: None
7**Context Notes**: User model changes required migration; deferred to phase 3
This takes five minutes to write but saves 30 minutes of re-explanation next session. You’re explicitly capturing the “mental state” of where you are, not just file state.
Context Priming for Session Start
When resuming work, don’t just dive in. Prime the context explicitly: “Please read PROJECT_STATE.md and the session note from 2025-01-15. We’re starting phase 2: implementing OAuth2Provider following the IAuthProvider interface we created last session.”
This applies Compass Pattern principles: navigate to context rather than duplicating it. The agent reads the external memory files and loads relevant context efficiently.
Recovery When Context Breaks
Sometimes agents lose their way mid-session, despite your best efforts. Quick recovery strategies:
- Reference the source: “We decided X in PROJECT_STATE.md. Please re-read that section.”
- Explicit reset: “Let’s pause. Read PROJECT_STATE.md again and tell me what we’re working on.”
- Salvage and redirect: “That approach won’t work because [constraint]. Here’s what we decided earlier: [paste from decision log].”
The key is having external memory to reference. Without it, you’re stuck re-explaining from scratch.
Strategy 3: Checkpoint-Driven Development
In Task Decomposition for AI Collaboration, I covered breaking work into bounded units. Checkpoints extend this concept to project phases: natural waypoints where you can stop, validate, and resume later without losing continuity.
A good checkpoint is completable in a focused session (2-4 hours), has clear success criteria you can verify, creates minimal dependencies on future work, and documents its interface with the next phase. The key difference from task decomposition? Checkpoints are about session boundaries; tasks are about work boundaries.
Consider the anti-pattern: “Refactor the entire authentication system.” This creates an un-resumable monolith. Better decomposition: Phase 1 extracts interfaces (resumable), Phase 2 implements new provider (resumable), Phase 3 handles migration (resumable). Each phase stands alone, builds on previous work through documented interfaces, and can be verified independently.
Before starting a checkpoint, create a brief plan document:
1## Checkpoint: Implement OAuth2Provider
2
3**Goal**: Working OAuth2 authentication following IAuthProvider interface
4**Success Criteria**: Tests pass, login flow works, backwards compatible
5**Dependencies**: IAuthProvider interface (completed Phase 1)
6**Risks**: Token refresh complexity, error handling edge cases
Update this as you work. When the checkpoint completes, it becomes part of your decision history and feeds into PROJECT_STATE.md. This creates a resumable audit trail: if you return weeks later, you can see exactly what each phase accomplished and why.
Strategy 4: Tool-Augmented Memory
The strategies so far rely on files: PROJECT_STATE.md, decision logs, session notes. This works brilliantly and remains my recommended starting point. But emerging protocols like Model Context Protocol (MCP) enable more sophisticated approaches for projects that need them.
MCP lets agents read and write to databases, monitor file systems, and integrate with external services. Instead of manually updating PROJECT_STATE.md, an agent with MCP tools could write checkpoint data to SQLite automatically, query previous decisions from a database rather than parsing markdown, or track state changes with proper versioning.
The current reality? Early days, but rapid evolution. Claude Desktop supports MCP; other tools are following. For long-running projects, this means true persistence across sessions without manual file management.
Start simple with files and Git. My post on documentation as decision history covers using commit messages as decision records and tags for major checkpoints. These patterns work today and integrate naturally with development workflows.
Add database-backed state management when file-based approaches become limiting. The principles stay constant (external, persistent, queryable memory); the implementation tools evolve.
Strategy 5: Workflow Design for Context Limitations
The best technical solutions fail if your workflow fights against agent capabilities. These patterns help structure interaction to work with, not against, context limitations.
Focus each session on one goal. Vertical slices beat horizontal layers. Completing “user authentication end-to-end” (vertical) works better than “all the database models” (horizontal) because it creates a testable, resumable checkpoint.
Progressive enhancement prevents scope creep. Get something working first. Enhance it in subsequent sessions building on documented previous work. This resists the “trying to do everything at once” trap that overwhelms both you and the agent.
Start sessions with questions, not implementation. Let the agent build context through exploration: “Based on PROJECT_STATE.md, what should we tackle in phase 3?” This engages the agent’s understanding before diving into code.
Strategy 6: Template Systems
These templates make the strategies above immediately actionable:
Project Starter Template (PROJECT_STATE.md):
1# Project State
2
3## Current Goal
4
5[What we're working towards this sprint/week]
6
7## Active Phase
8
9**Checkpoint**: [Name]
10**Status**: [In progress / Blocked / Complete]
11
12## Recent Decisions (Last 5)
13
141. [Date]: [Decision] - [Brief rationale]
15
16## Next Steps
17
18- [ ] [Specific actionable item]
19
20## Open Questions
21
22- [Thing we need to resolve]
Session Handoff Template:
1## Session [Date]
2
3**Completed**: [What got done]
4**Decisions**: [Why we made key choices]
5**Next**: [Immediate next steps]
6**Blockers**: [Anything blocking progress]
7**Context Notes**: [Important mental state to remember]
Recovery Prompt (when agent loses context):
“Let’s reset. Please read PROJECT_STATE.md and tell me: (1) What is our current goal? (2) What phase are we in? (3) What should we be working on right now?”
These templates work because they’re simple enough to actually use whilst structured enough to maintain continuity. Adapt them to your projects; the pattern matters more than the specific format.
Real-World Example: Authentication Migration
Here’s how these strategies work together on a realistic project that demonstrates the full power of systematic context management. You’re migrating from built-in authentication to OAuth2 across a multi-service application. This will span three weeks, multiple sessions, and complex dependencies. Without external memory systems, this project would collapse under its own complexity.
Week 1, Session 1: Setup and Phase 1 (2 hours)
You start by creating the foundation for systematic context management. Your initial PROJECT_STATE.md looks like this:
1# OAuth2 Migration Project
2
3## Current Goal
4
5Migrate from built-in auth to OAuth2 whilst maintaining backwards compatibility
6
7## Active Phase
8
9**Checkpoint 1**: Extract authentication interfaces
10**Status**: In progress
11**Timeline**: Week 1
12
13## Approach
14
15Phased rollout to minimise risk:
16
171. Extract interfaces (backwards compatible)
182. Implement OAuth2Provider
193. Data migration and cutover
20
21## Recent Decisions
22
231. 2025-01-10: Generic `IAuthProvider` interface over provider-specific implementations - maintains flexibility for future providers
24
25## Next Steps
26
27- [ ] Define IAuthProvider interface
28- [ ] Update User model to support external provider IDs
29- [ ] Create AuthService abstraction layer
30
31## Open Questions
32
33- Token refresh strategy for long-lived sessions?
34- How to handle users with both old and new auth methods during migration?
Working with the agent, you extract the IAuthProvider interface and update your user models. The agent suggests adding a ProviderId field to the User table, but you realize this creates a migration dependency. You document this in your session handoff note:
1## Session 2025-01-10
2
3**Completed**:
4
5- Created IAuthProvider interface with async methods
6- Updated User model schema (added ExternalProviderId, ProviderType fields)
7
8**Decisions**:
9
10- Generic interface supports multiple future providers
11- Async-first design for all auth operations
12- User model changes require migration - **defer to Phase 3**
13
14**Next**:
15Implement OAuth2Provider class following IAuthProvider
16
17**Blockers**: None
18
19**Context Notes**:
20Initially planned to run migration now, but decided to batch all DB changes in Phase 3 to reduce deployment complexity
This five-minute handoff note will save 30 minutes of re-explanation next session.
Between Sessions (4 days)
You work on other parts of the project. The code sits in Git. PROJECT_STATE.md and your session note persist. The agent remembers absolutely nothing, but that’s fine because you’ve created systematic external memory.
Week 1, Session 2: Phase 2 (3 hours)
Monday morning, you resume work. Instead of fumbling to remember where you left off, you have a clear protocol:
“Please read PROJECT_STATE.md and the session note from 2025-01-10. We’re starting Phase 2: implementing OAuth2Provider following the IAuthProvider interface we created last session. What should we focus on first?”
The agent reads both files and immediately understands the context: “Based on your documentation, we should implement the OAuth2Provider class following the IAuthProvider interface. I see you’ve deferred database migration to Phase 3, so we’ll need to ensure our provider implementation works with the updated User model fields without requiring immediate migration. Should we start with the token exchange flow?”
Context loaded in seconds, not minutes. No fumbling, no re-explaining. The agent picked up exactly where you left off and immediately contributed meaningfully.
You work through Phase 2 together, implementing the OAuth2Provider with token exchange, refresh logic, and error handling. Towards the end of the session, the agent suggests a caching strategy that would require changes to the User model. You update PROJECT_STATE.md to note this as a Phase 3 consideration and add it to your decision log:
1## 2025-01-14: Token Caching Decision
2
3**Decision**: Implement in-memory token cache, defer persistent caching to post-launch
4**Rationale**: Persistent caching requires User model changes already batched for Phase 3; in-memory suffices for MVP
5**Alternatives Considered**: Redis cache (over-engineering for current scale), persistent DB cache (conflicts with Phase 3 migration batching)
6**Impact**: Token refresh may be slightly more frequent, acceptable for launch
7**Future**: Revisit when user sessions exceed 10k concurrent
Week 2, Session 1: Context Recovery in Action (2.5 hours)
Mid-session disaster: The agent suggests changing the IAuthProvider interface to add a new method. This would break the backwards compatibility you established as a core constraint in Phase 1.
Old approach: 15 minutes of frustrated re-explanation.
With external memory: “That breaks our backwards compatibility requirement. Please read the PROJECT_STATE.md approach section and the decision log entry from 2025-01-10.”
The agent re-reads the documents: “You’re right, my suggestion violates the backwards compatibility constraint established in Phase 1. Instead, we should use an optional parameter on the existing Authenticate method. This maintains the interface contract whilst supporting the new OAuth2 flow.”
Crisis averted in under a minute. The external memory system saved you from context collapse that would have derailed the entire session.
Week 3: Phase 3 and Completion
Phase 3 (data migration) proceeds smoothly because every decision from Phases 1 and 2 is documented. When you need to remember why you batched the User model changes, it’s right there in the decision log. When the agent needs to understand the migration sequence, PROJECT_STATE.md provides the complete context.
Your final PROJECT_STATE.md shows the complete journey:
1# OAuth2 Migration Project
2
3## Current Goal
4
5COMPLETED: OAuth2 authentication live in production
6
7## Completed Phases
8
9✅ Checkpoint 1: Extract interfaces (Week 1)
10✅ Checkpoint 2: Implement OAuth2Provider (Week 1-2)
11✅ Checkpoint 3: Data migration and cutover (Week 3)
12
13## Project Outcomes
14
15- Zero downtime migration
16- All existing users migrated successfully
17- OAuth2 flow tested and verified
18- Backwards compatibility maintained throughout
19
20## Key Decisions Archive
21
221. 2025-01-10: Generic interface approach
232. 2025-01-14: Deferred persistent caching
243. 2025-01-18: Batched migrations in Phase 3
254. 2025-01-21: Gradual rollout strategy (10% → 50% → 100%)
26
27## Lessons Learned
28
29- Batching DB changes reduced deployment risk significantly
30- In-memory caching sufficient for current scale
31- Gradual rollout caught edge case in token refresh (now fixed)
The Measured Impact
Without these strategies, this project would have looked like:
- 10-15 minutes per session explaining previous context
- Multiple instances of lost decisions causing rework
- Broken continuity across the three-week span
- High probability of forgetting critical constraints
With systematic context management:
- Context reload in under 1 minute using external memory files
- Zero instances of forgotten decisions causing rework
- Complete continuity across 3 weeks and 8 sessions
- All architectural constraints maintained throughout
- Total overhead: ~25 minutes of documentation across entire project
- Time saved: 80+ minutes of re-explanation avoided
The ROI is overwhelming. Twenty-five minutes of systematic documentation saved 80+ minutes of frustrated re-explanation, whilst delivering higher quality outcomes through maintained decision consistency.
What’s Coming: The Evolution of AI Memory
The landscape is evolving rapidly. Understanding these trends helps you build systems that remain valuable as capabilities improve, rather than becoming obsolete.
Native State Management Emerging Now
Several AI platforms are already experimenting with built-in project memory. Some maintain context automatically across conversations. Others let you pin important context that persists between sessions. These features reduce the manual overhead of file-based external memory, but they don’t eliminate the need for systematic approach.
The key insight: Native state management tools will become more powerful, but the principles remain constant. Whether context lives in PROJECT_STATE.md or a platform’s memory system, you still need structured handoffs, clear checkpoints, and systematic decision tracking. The implementation changes; the methodology doesn’t.
MCP Ecosystem Maturation
Model Context Protocol (MCP) represents a significant architectural shift. Instead of agents reading static files, they’ll interact with live systems: writing to databases, updating project management tools, monitoring file systems for changes.
Early adopters are already seeing benefits. Agents using MCP can automatically checkpoint their progress to SQLite, query previous decisions from structured databases rather than parsing markdown, and track state changes with proper versioning. Claude Desktop supports MCP today; other platforms are following rapidly.
But here’s what many miss: MCP makes the file-based approaches more powerful, not obsolete. My PROJECT_STATE.md could become a SQLite database the agent updates automatically. My session handoff notes could be database records with structured queries. The systematic thinking that created effective file-based systems translates directly to database-backed implementations.
Persistent Project Understanding
The holy grail: agents that truly remember across sessions without requiring explicit context reload. We’re seeing early experiments with this. Agents that maintain project-specific memory. Systems that understand “we’re working on the OAuth2 migration” without being told every session.
This capability is coming, but it won’t eliminate the need for external memory systems. Even humans with perfect memory benefit from written documentation. The decisions you make, the constraints you discover, the trade-offs you consider, these need to be explicit and queryable. Perfect agent memory might reduce the overhead of maintaining external systems, but it won’t eliminate their value.
Multi-Session Planning Capabilities
Imagine telling an agent: “We need to migrate to OAuth2 over the next three weeks. Create a phased plan with checkpoints.” The agent generates not just a plan, but a project management structure: checkpoints with success criteria, decision templates for architectural choices, automated progress tracking.
This isn’t science fiction. Early versions exist today. They’re rough, but the trajectory is clear. Within two years, agents will naturally think in multi-session, multi-phase projects rather than struggling with continuity.
What Won’t Change
Good documentation helps humans regardless of agent capabilities. When you leave the project for a month, or hand it to a colleague, or return to it years later, systematic documentation remains invaluable. The external memory systems you build for AI collaboration serve human collaboration equally well.
Structured work is better work independent of tools. Breaking projects into resumable checkpoints, maintaining decision logs, tracking progress systematically, these practices improve outcomes whether you’re working solo, with a team, or with AI assistance. The agents might change; the principles endure.
Clear communication matters more as systems get complex. The better agents become at autonomous work, the more important it becomes that their decisions are transparent, their reasoning is documented, and their progress is trackable. External memory systems create the audit trail that makes complex AI collaboration trustworthy.
Staying Adaptable
Build on principles, not specific tools. The file structure I’ve shown (PROJECT_STATE.md, DECISIONS.md, PROGRESS.md) works brilliantly today. Tomorrow it might be database tables or platform-native features. But the underlying principles, external and persistent memory, systematic decision tracking, checkpoint-driven development, structured handoffs, those remain constant.
When new capabilities emerge, evaluate them against these principles. Does this tool make external memory more accessible? Does it improve decision tracking? Does it enable better checkpointing? If yes, adopt it. If it’s just new for the sake of new, stick with what works.
Start simple. File-based approaches work today, require no special infrastructure, and integrate with existing development workflows. Add database-backed state management when you hit their limitations. Adopt MCP tools when they provide clear value beyond what files deliver. Let your actual needs drive adoption, not hype about emerging capabilities.
Conclusion
The difference between frustration and flow in AI-assisted development isn’t the agent’s capability. It’s how you structure the work. External memory systems, structured handoffs, checkpoint-driven development, and systematic recovery protocols transform context collapse into reliable continuity.
Start with one strategy. Create a PROJECT_STATE.md for your current project. Add session handoff notes. Build from there. Even simple external memory helps enormously, freeing agents to focus on solving problems rather than reconstructing context.
The goal isn’t perfect agents. The goal is effective collaboration on complex projects that span sessions, build on previous decisions, and deliver real value. These strategies make that collaboration possible today, using tools and techniques you can implement immediately.
Your next long-running project doesn’t have to retreat to manual orchestration. With systematic context management, AI agents become reliable partners for the complex, multi-phase work that matters most.
About the Author
Tim Huegdon is the founder of Wyrd Technology, a consultancy that helps engineering teams achieve operational excellence through systematic AI adoption. With over 25 years of experience in software engineering and technical leadership, Tim specialises in developing practical frameworks for AI collaboration that enhance rather than replace proven development practices. His work on context management, documentation architecture, and human-AI collaboration patterns helps organisations build sustainable AI workflows whilst maintaining the quality standards that enable effective team collaboration.