Connected Patterns: Turning Uncertainty Into Correct Actions
“The fastest way to be wrong is to use the right tool at the wrong time.”
Most agent systems fail for a simple reason: the agent does not know what kind of problem it is holding.
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It treats a factual question like a reasoning puzzle and makes something up.
It treats a reasoning puzzle like a lookup task and pastes irrelevant information.
It treats a missing requirement like a detail it can infer and then commits the wrong action.
It treats a tool error like a signal to retry forever.
Tool routing is the policy that decides what to do next when the agent has multiple options: search, compute, ask, or stop.
This sounds basic. It is not. Routing is the difference between an agent that feels “impressive” and an agent that is correct.
The Hidden Question Behind Every Step
Every agent step can be reduced to one question:
What is the highest-trust move available right now?
High trust is not “high confidence.” High trust is “highly checkable.”
A good routing policy prefers moves that are:
• Verifiable
• Reversible
• Low side-effect
• Low cost relative to value
• Aligned with constraints and goals
That one principle collapses many debates. If the agent can compute something exactly, compute it. If it must use external information, retrieve it with verification gates. If the request is underspecified, ask before guessing. If the step carries risk, stop and escalate.
A Practical Routing Taxonomy
To route well, an agent needs to classify the current need. You can do this with a small taxonomy.
| Need type | The right move | What “wrong move” looks like |
|---|---|---|
| Stable knowledge | Retrieve from trusted sources or internal knowledge base | Inventing facts or quoting without evidence |
| Fresh or changing facts | Search with recency filters and citations | Relying on memory for time-sensitive details |
| Deterministic computation | Compute with a tool and show intermediate checks | Guessing numbers or approximations |
| Ambiguous requirements | Ask targeted questions or offer options | Assuming hidden preferences |
| High-risk action | Require approval gate, simulate, or sandbox | Acting directly in production |
| Conflicting evidence | Verify, cross-check, or escalate | Picking a favorite source |
| Unclear success criteria | Ask what “done” means | Declaring victory early |
This taxonomy is small enough to implement and strong enough to reduce error rates dramatically.
When to Search
Search is appropriate when the needed information is not already in state and cannot be computed from first principles.
Search becomes essential when:
• The fact is time-sensitive (prices, policies, current office holders, schedules)
• The domain is niche and likely outside the agent’s prior context
• The user asked for sources, citations, or direct quotes
• The agent suspects a term is unfamiliar or could be a typo
• The risk of a wrong answer is high
But search is also dangerous. It brings in stale sources, low-quality sources, and conflicting claims. A routing policy should treat search as “retrieve candidates,” not “accept truth.”
A robust search route includes:
• A plan for what must be verified
• A recency rule when needed
• A source quality preference list
• A contradiction check
• A citation requirement for claims that matter
If the agent cannot do those things, the right route may be: ask a human or stop.
When to Compute
Compute is appropriate when the answer can be derived from provided inputs, formal rules, or deterministic algorithms.
Compute should be preferred over search when:
• The task is arithmetic, parsing, formatting, or transformation
• The source data is already available in state or as a file
• The result can be validated easily
• The cost of a compute tool call is low relative to the value
Compute is also a verification tool. Even when the agent retrieves information, it can often compute cross-checks:
• Recalculate totals from a table instead of trusting a summary
• Validate that dates and ranges are consistent
• Check that units match
• Detect internal contradictions
Routing to compute is one of the simplest ways to turn vague reasoning into checkable work.
When to Ask
Asking is not a weakness. It is a routing decision that prevents downstream waste.
Ask when the agent detects any of these conditions:
• Missing constraints that affect the outcome
• Multiple plausible interpretations with different results
• A requirement that only the user can define (tone, audience, risk tolerance)
• A decision that is value-laden rather than factual
• A step that would be irreversible or expensive without confirmation
A good “ask route” has two rules:
• Ask as few questions as possible, but ask the ones that change the plan.
• Offer a default option when safe, so the user can answer quickly.
The worst agents ask endlessly because they are unsure. The second worst agents never ask and guess. The best agents ask only when a missing detail would cause a wrong commitment.
When to Stop or Escalate
Stopping is a legitimate route. Escalation is a legitimate route. Many systems fail because they did not treat these as first-class actions.
Stop when:
• Budgets are exceeded
• Verification fails and cannot be repaired
• The task requires permissions not granted
• The agent cannot obtain reliable evidence
• The next step is too risky without approval
Escalate when:
• A human decision is required
• Conflicting evidence affects a high-stakes outcome
• The system needs new tool access or policy changes
• The agent’s uncertainty remains high after attempted verification
The routing policy should make stopping graceful: produce a partial result, list what is needed, and show the evidence collected so far.
Routing as a Verification Ladder
The strongest way to think about routing is as a ladder from low-trust moves to high-trust moves.
A practical ladder:
• Ask: clarify the goal and constraints
• Retrieve: gather candidate information
• Compute: transform and cross-check
• Verify: compare sources and test consistency
• Commit: produce the artifact or execute the action
• Report: summarize what was done, with evidence and remaining uncertainty
This ladder aligns with how careful humans work. The agent harness simply enforces it.
The Route in the Life of a Production Team
Routing policy becomes even more important when multiple people rely on agent outputs.
Without routing:
• The agent answers quickly but cannot explain why.
• The agent chooses tools based on convenience, not correctness.
• Teams lose time chasing contradictions and cleaning up bad outputs.
With routing:
• The agent chooses the most checkable next step.
• The agent surfaces uncertainty early.
• Teams get fewer surprises, fewer retries, and clearer run reports.
Routing also makes system behavior predictable. Predictability is what allows you to monitor quality and improve over time.
Routing Examples You Will See Every Day
Routing becomes easier when you train the system to recognize a few recurring situations.
A user asks for “the current policy” on something that changes frequently.
Best route: search with a recency check, prefer authoritative sources, cite, and surface uncertainty if sources disagree.
A user provides a CSV and asks for totals, averages, or a ranking.
Best route: compute from the provided file, then compute a second check on the result (for example, verify sums match row counts).
A user asks for a recommendation but gives no budget or constraints.
Best route: ask a small set of constraint questions, offer two safe defaults, then search for candidates once the target is clear.
A tool returns an error that could be transient.
Best route: retry with backoff and a cap, then switch to a fallback tool or escalate. Never hammer the same tool endlessly.
Two sources disagree on a key fact.
Best route: verify by finding the primary source, compare dates, and report the disagreement if it cannot be resolved safely.
In each case, the routing decision is not about cleverness. It is about choosing the next step that preserves correctness and keeps the run within safe boundaries.
A Routing Policy You Can Encode
If you want a compact set of rules you can put into code, use this pattern:
• If the task is deterministic and inputs are known, compute.
• If a claim depends on external facts, search and cite.
• If the request is underspecified, ask before acting.
• If evidence conflicts, verify or escalate.
• If the action has side effects, gate it.
• If budgets or policies are violated, stop.
This is not “prompt engineering.” This is system design. It belongs in the harness as enforceable logic, not as optional advice.
Choosing Truth Over Speed
Agents feel magical when they answer instantly. They become valuable when they answer correctly.
Tool routing is how you build that value. It is how you train the system to prefer verification over vibes, evidence over confidence, and safe progress over flashy improvisation.
Once routing is explicit, you can evolve everything else: new tools, new models, new workflows. The system stays grounded because it knows how to choose the next move.
Keep Exploring Tool Use and Verification
• Safe Web Retrieval for Agents
https://ai-rng.com/safe-web-retrieval-for-agents/
• Verification Gates for Tool Outputs
https://ai-rng.com/verification-gates-for-tool-outputs/
• Designing Tool Contracts for Agents
https://ai-rng.com/designing-tool-contracts-for-agents/
• Agent Error Taxonomy: The Failures You Will Actually See
https://ai-rng.com/agent-error-taxonomy-the-failures-you-will-actually-see/
• Benchmarking Scientific Claims
https://ai-rng.com/benchmarking-scientific-claims/
• AI for Scientific Discovery: The Practical Playbook
https://ai-rng.com/ai-for-scientific-discovery-the-practical-playbook/
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